Faculty Bibliography

Background/UNASSIGNED:Lung cancer (LC) development is a process that depends on genetic mutations. The DNA methylation, an important epigenetic modification, is associated with the expression of non-coding RNAs, such as microRNAs. MicroRNAs are particularly essential for cell physiology, since they play a critical role in tumor suppressor gene activity. Furthermore, epigenetic disruptions are the primary event in cell modification, being related to tumorigenesis. In this context, microRNAs can be a useful tool in the LC suppression, consequently improving prognosis and predicting treatment. Conclusion/UNASSIGNED:This manuscript reviews the main microRNAs involved in LC and its potential clinical applications to improve outcomes, such as survival and better quality of life.

Summary/: Background:Development and validation of a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (ES-NSCLC) that is prognostic of disease-free survival (DFS) and predictive of the added benefit of adjuvant chemotherapy (ACT) following surgery. Methods:. Findings:,p<0·05, N=86) and other immune specific biological pathways. Interpretation:QuRiS and QuRNom were validated as being prognostic of DFS and predictive of the added benefit of ACT.

BACKGROUND:Use of adjuvant chemotherapy in patients with early-stage lung cancer is controversial because no definite biomarker exists to identify patients who would receive added benefit from it. We aimed to develop and validate a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (NSCLC) that is prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy following surgery. METHODS:. FINDINGS:). INTERPRETATION:QuRiS and QuRNom were validated as being prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy, especially in clinically defined low-risk groups. Since QuRiS is based on routine chest CT imaging, with additional multisite independent validation it could potentially be employed for decision management in non-invasive treatment of resectable lung cancer. FUNDING:National Cancer Institute of the US National Institutes of Health, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, Department of Defence, National Institute of Diabetes and Digestive and Kidney Diseases, Wallace H Coulter Foundation, Case Western Reserve University, and Dana Foundation.

OBJECTIVES/OBJECTIVE:To evaluate whether combining stability and discriminability criteria in building radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small cell lung cancer on non-contrast computer tomography (CT). MATERIALS AND METHODS/METHODS:) validation sets. A linear discriminant analysis (LDA) classifier was built based on the most stable and discriminate features. In addition, a radiomic risk score (RRS) was generated by using least absolute shrinkage and selection operator, Cox regression model to predict time to progression (TTP) following surgery. RESULTS:, 0.76 vs. 0.63). The RRS generated by most stable-discriminating features was significantly associated with TTP compared to discriminating alone criteria (HR = 1.66, C-index of 0.72 vs. HR = 1.04, C-index of 0.62). CONCLUSION/CONCLUSIONS:Accounting for both stability and discriminability yielded a more generalizable classifier for predicting cancer recurrence and TTP in early stage NSCLC.

Activating receptor tyrosine kinase RET (rarranged during transfection) gene alterations have been identified as oncogenic in multiple malignancies. RET gene rearrangements retaining the kinase domain are oncogenic drivers in papillary thyroid cancer, non-small-cell lung cancer, and multiple other cancers. Activating RET mutations are associated with different phenotypes of multiple endocrine neoplasia type 2 as well as sporadic medullary thyroid cancer. RET is thus an attractive therapeutic target in patients with oncogenic RET alterations. Multikinase inhibitors with RET inhibitor activity, such as cabozantinib and vandetanib, have been explored in the clinic for tumors with activating RET gene alterations with modest clinical efficacy. As a result of the nonselective nature of these multikinase inhibitors, patients had off-target adverse effects, such as hypertension, rash, and diarrhea. This resulted in a narrow therapeutic index of these drugs, limiting ability to dose for clinically effective RET inhibition. In contrast, the recent discovery and clinical validation of highly potent selective RET inhibitors (pralsetinib, selpercatinib) demonstrating improved efficacy and a more favorable toxicity profile are poised to alter the landscape of RET-dependent cancers. These drugs appear to have broad activity across tumors with activating RET alterations. The mechanisms of resistance to these next-generation highly selective RET inhibitors is an area of active research. This review summarizes the current understanding of RET alterations and the state-of-the-art treatment strategies in RET-dependent cancers.

Despite substantial progress in lung cancer immunotherapy, the overall response rate in KRAS-mutant lung adenocarcinoma (ADC) patients remains low. Combining standard immunotherapy with adjuvant approaches that enhance adaptive immune responses-such as epigenetic modulation of anti-tumor immunity-is therefore an attractive strategy. To identify epigenetic regulators of tumor immunity, we constructed an epigenetic-focused sgRNA library, and performed an in vivo CRISPR screen in a KrasG12D/P53-/- (KP) lung ADC model. Our data showed that loss of the histone chaperone Asf1a in tumor cells sensitizes tumors to anti-PD-1 treatment. Mechanistic studies revealed that tumor cell-intrinsic Asf1a deficiency induced immunogenic macrophage differentiation in the tumor microenvironment by upregulating GM-CSF expression and potentiated T cell activation in combination with anti-PD-1. Our results provide rationale for a novel combination therapy consisting of ASF1A inhibition and anti-PD-1 immunotherapy.

Cyclin-dependent kinase 7 (CDK7) is a central regulator of the cell cycle and gene transcription. However, little is known about its impact on genomic instability and cancer immunity. Using a selective CDK7 inhibitor, YKL-5-124, we demonstrated that CDK7 inhibition predominately disrupts cell-cycle progression and induces DNA replication stress and genome instability in small cell lung cancer (SCLC) while simultaneously triggering immune-response signaling. These tumor-intrinsic events provoke a robust immune surveillance program elicited by T cells, which is further enhanced by the addition of immune-checkpoint blockade. Combining YKL-5-124 with anti-PD-1 offers significant survival benefit in multiple highly aggressive murine models of SCLC, providing a rationale for new combination regimens consisting of CDK7 inhibitors and immunotherapies.

No predictive biomarkers can robustly identify non-small cell lung cancer (NSCLC) patients who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of computed tomography (CT) patterns both within and outside tumor nodules before and after 2-3 cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 NSCLC patients at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST (response evaluation criteria in solid tumors)-derived response. Association of delta-radiomic risk-score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an area under the curve (AUC) of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, 0.85 and 0.81 in D2 and D3. DRS was associated with OS (hazard ratio: 1.64, 95% CI: 1.22 - 2.21, P = 0.0011, C-Index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in NSCLC patients.

Aim: To determine real-world outcomes with first-line pembrolizumab monotherapy for metastatic non-small-cell lung cancer with PD-L1 tumor expression ≥50%. Methods: This retrospective study included adults with ECOG 0-1 initiating first-line pembrolizumab monotherapy on/after 24 October 2016 (EHR cohort) or from 1 December 2016 through 30 November 2017 (spotlight cohort) with ≥6-month follow-up. We estimated Kaplan-Meier overall survival (OS, both cohorts), and, for spotlight, real-world progression-free survival (rwPFS) by Kaplan-Meier and real-world tumor response (rwTR). Results: For 423 patients in the EHR cohort and 188 in spotlight, median OS was 18.9 months (95% CI: 14.9-25.5) and 19.1 months (12.6-not reached), respectively. For spotlight, median rwPFS was 6.8 months (5.3-8.1); rwTR of complete/partial response was 48% (41-56%). Conclusion: Observed OS, rwPFS and rwTR were consistent with clinical trial findings.

In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and 'hand-crafted' feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.